The present document relates to medical imaging. In particular, different portions of the image are identified to assist diagnosis.
Physicians are confronted with patients who may have multiple fractures or inner injuries all over their body, such as after a serious traffic accident. In such situations, the time needed to find the injuries and to classify the injuries by importance is a critical factor.
The ability to recognize salient regions of interest within a human body from medical images may provide many benefits. It can improve operator workflow and efficiency within a medical facility. The latest generation of CT scanners often produces data with sub millimeter resolution and a large amount of pixel data. Nevertheless, most of the time, radiologists are looking for a specific pathology and thus a specific part of the dataset. It would be very helpful to support the physician in finding injuries as fast as possible before surgery.
Specific regions of interest may be segmented. Atlas-based classifiers based on registration techniques may localize specific organs. Atlas-based classifiers create an input model by processing the input images and register the input model over an atlas (a mean shape that is already segmented and labeled). The registration produces a mapping from the input model space to the atlas space. To label the input, each point of the input model is found in the atlas to identify the label. The atlas-based classifier may work on a specific ROI and not on a general scan. In addition, the atlas-based classifier may be computationally intensive, may not be able to handle large-scale deformations, and may have mismatch problems with the target image. An atlas-based classifier may not be able to handle arbitrary image scans, since atlas-based classifiers use a specific region of interest (ROI) for reliable operation.
Other segmentation has been provided. Anatomical structures from torso CT images may be automatically extracted and classified. The original CT image is separated into air, bone, fat, muscle, and organ regions based on the difference of their density and spatial location. The torso region is then segmented using spatial information, such as bone and fat distribution. In another approach, the bone intensity profile is used to separate various sections of bone structures. These approaches may be limited in their application since the image scan is assumed to cover a particular body area. These approaches may not be robust for images with unknown or missing body areas.